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Topic Review
Deep Eutectic Solvents (DES)
Deep eutectic solvent (DES) are a highly non-ideal mixture of two biodegradable components (HBA and HBD) associated with strong hydrogen bonding interactions. 
  • 4.5K
  • 08 Mar 2022
Topic Review
Deep Eutectic Solvents as Promising Green Solvents
Deep eutectic solvents (DESs) have recently attracted attention as a promising green alternative to conventional hazardous solvents by virtue of their simple preparation, low cost, and biodegradability. Even though the application of DESs in analytical chemistry is still in its early stages, the number of publications on this topic is growing. Analytical procedures applying dispersive liquid–liquid microextraction based on the solidification of floating organic droplets (DLLME-SFOD) are among the more appealing approaches where DESs have been found to be applicable.
  • 937
  • 20 Jan 2022
Topic Review
Deep Learning for Land Use
Image super-resolution (SR) techniques can improve the spatial resolution of remote sensing images to provide more feature details and information, which is important for a wide range of remote sensing applications, including land use/cover classification (LUCC). Convolutional neural networks (CNNs) have achieved impressive results in the field of image SR, but the inherent localization of convolution limits the performance of CNN-based SR models. 
  • 161
  • 24 Nov 2023
Topic Review
Deep Learning for Remote Sensing Image Scene Classification
Scene classification in remote sensing images aims to categorize image scenes automatically into relevant classes like residential areas, cultivation land, forests, etc. The implementation of deep learning (DL) for scene classification is an emerging tendency, with an effort to achieve maximum accuracy.
  • 556
  • 13 Oct 2023
Topic Review
Deep Learning Methods for Smoke Recognition
Fire accidents cause alarming damage. They result in the loss of human lives, damage to property, and significant financial losses. Early fire ignition detection systems, particularly smoke detection systems, play a crucial role in enabling effective firefighting efforts. In this paper, a novel DL (Deep Learning) method, namely BoucaNet, is introduced for recognizing smoke on satellite images while addressing the associated challenging limitations. BoucaNet combines the strengths of the deep CNN EfficientNet v2 and the vision transformer EfficientFormer v2 for identifying smoke, cloud, haze, dust, land, and seaside classes. Extensive results demonstrate that BoucaNet achieved high performances. BoucaNet also showed a robust ability to overcome challenges, including complex backgrounds; detecting small smoke zones; handling varying smoke features such as size, shape, and color; and handling visual similarities between smoke, clouds, dust, and haze.
  • 271
  • 21 Dec 2023
Topic Review
Deep Learning-Based Building Extraction from Remote Sensing Images
Building extraction from remote sensing (RS) images is a fundamental task for geospatial applications, aiming to obtain morphology, location, and other information about buildings from RS images, which is significant for geographic monitoring and construction of human activity areas. In recent years, deep learning (DL) technology has made remarkable progress and breakthroughs in the field of RS and also become a central and state-of-the-art method for building extraction. 
  • 652
  • 15 Dec 2021
Topic Review
Deep Learning-Based Change Detection
Change detection based on remote sensing images plays an important role in the field of remote sensing analysis, and it has been widely used in many areas, such as resources monitoring, urban planning, disaster assessment, etc. With the improved spatial resolution of remote sensing images, many deep learning methods have been proposed for aerial and satellite image change detection. Depending on the granularity of the detection unit, these methods can be roughly classified into two main categories : scene-level methods (SLCDs) and region-level methods (RLCDs). These two categories are not necessarily independent of each other, and sometimes, the same change detection process may be present in different methods simultaneously.
  • 1.1K
  • 01 Apr 2022
Topic Review
Deep Learning-Based Weed Detection Using UAV Images
Deep learning-based weed detection using UAV images. Recently, the Unmanned Aerial Vehicle (UAV) has made significant progress in its design and capability, including payload flexibility, communication and connectivity, navigation and autonomy, speed and flight time, which has potential to revolutionize the precision agriculture.
  • 287
  • 13 Oct 2023
Topic Review
Deep Sea Fish
Deep-sea fish are animals that live in the darkness below the sunlit surface waters, that is below the epipelagic or photic zone of the sea. The lanternfish is, by far, the most common deep-sea fish. Other deep sea fishes include the flashlight fish, cookiecutter shark, bristlemouths, anglerfish, viperfish, and some species of eelpout. Only about 2% of known marine species inhabit the pelagic environment. This means that they live in the water column as opposed to the benthic organisms that live in or on the sea floor. Deep-sea organisms generally inhabit bathypelagic (1000–4000m deep) and abyssopelagic (4000–6000m deep) zones. However, characteristics of deep-sea organisms, such as bioluminescence can be seen in the mesopelagic (200–1000m deep) zone as well. The mesopelagic zone is the disphotic zone, meaning light there is minimal but still measurable. The oxygen minimum layer exists somewhere between a depth of 700m and 1000m deep depending on the place in the ocean. This area is also where nutrients are most abundant. The bathypelagic and abyssopelagic zones are aphotic, meaning that no light penetrates this area of the ocean. These zones make up about 75% of the inhabitable ocean space. The epipelagic zone (0–200m) is the area where light penetrates the water and photosynthesis occurs. This is also known as the photic zone. Because this typically extends only a few hundred meters below the water, the deep sea, about 90% of the ocean volume, is in darkness. The deep sea is also an extremely hostile environment, with temperatures that rarely exceed 3 °C (37.4 °F) and fall as low as −1.8 °C (28.76 °F) (with the exception of hydrothermal vent ecosystems that can exceed 350 °C, or 662 °F), low oxygen levels, and pressures between 20 and 1,000 atmospheres (between 2 and 100 megapascals).
  • 2.6K
  • 08 Nov 2022
Topic Review
Deep Supervision-Based Simple Attention Network
Semantic segmentation for remote sensing images (RSIs) plays an important role in many applications, such as urban planning, environmental protection, agricultural valuation, and military reconnaissance. With the boom in remote sensing technology, numerous RSIs are generated; this is difficult for current complex networks to handle. Efficient networks are the key to solving this challenge.
  • 541
  • 21 Nov 2022
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